from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 13.0 | 19.224901 |
| daal4py_KNeighborsClassifier | 0.0 | 3.0 | 14.974038 |
| KNeighborsClassifier_kd_tree | 0.0 | 7.0 | 2.729862 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 43.911961 |
| KMeans_tall | 0.0 | 1.0 | 39.927861 |
| daal4py_KMeans_tall | 0.0 | 1.0 | 14.283303 |
| KMeans_short | 0.0 | 0.0 | 20.823591 |
| daal4py_KMeans_short | 0.0 | 0.0 | 8.978808 |
| LogisticRegression | 0.0 | 0.0 | 59.618679 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 52.859150 |
| Ridge | 0.0 | 0.0 | 45.974159 |
| daal4py_Ridge | 0.0 | 0.0 | 14.951763 |
| total | 0.0 | 31.0 | 38.321814 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 0.126 | 0.001 | 1000000 | 1000000 | 100 | brute | -1 | 1 | 0.982 | 0.982 | 0.442 | 0.001 | 0.284 | 0.002 | See |
| 1 | KNeighborsClassifier | predict | 24.472 | 0.517 | 1000000 | 1000 | 100 | brute | -1 | 1 | 0.982 | 0.982 | 1.924 | 0.016 | 12.720 | 0.288 | See |
| 2 | KNeighborsClassifier | predict | 0.157 | 0.013 | 1000000 | 1 | 100 | brute | -1 | 1 | 0.982 | 0.982 | 0.083 | 0.001 | 1.902 | 0.161 | See |
| 3 | KNeighborsClassifier | fit | 0.130 | 0.002 | 1000000 | 1000000 | 100 | brute | -1 | 5 | 0.982 | 0.982 | 0.452 | 0.005 | 0.288 | 0.006 | See |
| 4 | KNeighborsClassifier | predict | 31.676 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 5 | 0.982 | 0.982 | 1.926 | 0.021 | 16.446 | 0.176 | See |
| 5 | KNeighborsClassifier | predict | 0.161 | 0.011 | 1000000 | 1 | 100 | brute | -1 | 5 | 0.982 | 0.982 | 0.082 | 0.000 | 1.961 | 0.129 | See |
| 6 | KNeighborsClassifier | fit | 0.129 | 0.002 | 1000000 | 1000000 | 100 | brute | -1 | 100 | 0.982 | 0.982 | 0.439 | 0.001 | 0.293 | 0.005 | See |
| 7 | KNeighborsClassifier | predict | 31.530 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 100 | 0.982 | 0.982 | 1.999 | 0.015 | 15.773 | 0.115 | See |
| 8 | KNeighborsClassifier | predict | 0.171 | 0.011 | 1000000 | 1 | 100 | brute | -1 | 100 | 0.982 | 0.982 | 0.082 | 0.000 | 2.077 | 0.139 | See |
| 9 | KNeighborsClassifier | fit | 0.131 | 0.003 | 1000000 | 1000000 | 100 | brute | 1 | 1 | 0.982 | 0.982 | 0.454 | 0.005 | 0.288 | 0.007 | See |
| 10 | KNeighborsClassifier | predict | 12.127 | 0.017 | 1000000 | 1000 | 100 | brute | 1 | 1 | 0.982 | 0.982 | 1.912 | 0.012 | 6.342 | 0.039 | See |
| 11 | KNeighborsClassifier | predict | 0.176 | 0.004 | 1000000 | 1 | 100 | brute | 1 | 1 | 0.982 | 0.982 | 0.084 | 0.005 | 2.101 | 0.139 | See |
| 12 | KNeighborsClassifier | fit | 0.130 | 0.003 | 1000000 | 1000000 | 100 | brute | 1 | 5 | 0.982 | 0.982 | 0.441 | 0.001 | 0.295 | 0.006 | See |
| 13 | KNeighborsClassifier | predict | 20.162 | 0.028 | 1000000 | 1000 | 100 | brute | 1 | 5 | 0.982 | 0.982 | 1.885 | 0.017 | 10.698 | 0.100 | See |
| 14 | KNeighborsClassifier | predict | 0.185 | 0.001 | 1000000 | 1 | 100 | brute | 1 | 5 | 0.982 | 0.982 | 0.082 | 0.001 | 2.262 | 0.039 | See |
| 15 | KNeighborsClassifier | fit | 0.129 | 0.002 | 1000000 | 1000000 | 100 | brute | 1 | 100 | 0.982 | 0.982 | 0.449 | 0.004 | 0.288 | 0.006 | See |
| 16 | KNeighborsClassifier | predict | 20.299 | 0.010 | 1000000 | 1000 | 100 | brute | 1 | 100 | 0.982 | 0.982 | 1.969 | 0.019 | 10.311 | 0.101 | See |
| 17 | KNeighborsClassifier | predict | 0.189 | 0.003 | 1000000 | 1 | 100 | brute | 1 | 100 | 0.982 | 0.982 | 0.083 | 0.001 | 2.280 | 0.038 | See |
| 18 | KNeighborsClassifier | fit | 0.056 | 0.001 | 1000000 | 1000000 | 2 | brute | -1 | 1 | 0.982 | 0.982 | 0.090 | 0.004 | 0.625 | 0.025 | See |
| 19 | KNeighborsClassifier | predict | 20.326 | 0.105 | 1000000 | 1000 | 2 | brute | -1 | 1 | 0.982 | 0.982 | 0.300 | 0.003 | 67.755 | 0.758 | See |
| 20 | KNeighborsClassifier | predict | 0.020 | 0.003 | 1000000 | 1 | 2 | brute | -1 | 1 | 0.982 | 0.982 | 0.006 | 0.000 | 3.346 | 0.439 | See |
| 21 | KNeighborsClassifier | fit | 0.056 | 0.000 | 1000000 | 1000000 | 2 | brute | -1 | 5 | 0.982 | 0.982 | 0.088 | 0.002 | 0.633 | 0.018 | See |
| 22 | KNeighborsClassifier | predict | 28.051 | 0.015 | 1000000 | 1000 | 2 | brute | -1 | 5 | 0.982 | 0.982 | 0.302 | 0.002 | 92.860 | 0.586 | See |
| 23 | KNeighborsClassifier | predict | 0.032 | 0.002 | 1000000 | 1 | 2 | brute | -1 | 5 | 0.982 | 0.982 | 0.006 | 0.000 | 5.288 | 0.354 | See |
| 24 | KNeighborsClassifier | fit | 0.051 | 0.000 | 1000000 | 1000000 | 2 | brute | -1 | 100 | 0.982 | 0.982 | 0.089 | 0.001 | 0.577 | 0.006 | See |
| 25 | KNeighborsClassifier | predict | 27.858 | 0.152 | 1000000 | 1000 | 2 | brute | -1 | 100 | 0.982 | 0.982 | 0.357 | 0.002 | 78.139 | 0.613 | See |
| 26 | KNeighborsClassifier | predict | 0.033 | 0.002 | 1000000 | 1 | 2 | brute | -1 | 100 | 0.982 | 0.982 | 0.007 | 0.001 | 4.858 | 0.660 | See |
| 27 | KNeighborsClassifier | fit | 0.066 | 0.003 | 1000000 | 1000000 | 2 | brute | 1 | 1 | 0.982 | 0.982 | 0.089 | 0.003 | 0.733 | 0.038 | See |
| 28 | KNeighborsClassifier | predict | 9.511 | 0.018 | 1000000 | 1000 | 2 | brute | 1 | 1 | 0.982 | 0.982 | 0.302 | 0.004 | 31.474 | 0.388 | See |
| 29 | KNeighborsClassifier | predict | 0.015 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 1 | 0.982 | 0.982 | 0.006 | 0.000 | 2.384 | 0.190 | See |
| 30 | KNeighborsClassifier | fit | 0.065 | 0.002 | 1000000 | 1000000 | 2 | brute | 1 | 5 | 0.982 | 0.982 | 0.089 | 0.001 | 0.730 | 0.028 | See |
| 31 | KNeighborsClassifier | predict | 17.827 | 0.012 | 1000000 | 1000 | 2 | brute | 1 | 5 | 0.982 | 0.982 | 0.303 | 0.004 | 58.894 | 0.795 | See |
| 32 | KNeighborsClassifier | predict | 0.027 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 5 | 0.982 | 0.982 | 0.006 | 0.000 | 4.674 | 0.211 | See |
| 33 | KNeighborsClassifier | fit | 0.066 | 0.002 | 1000000 | 1000000 | 2 | brute | 1 | 100 | 0.982 | 0.982 | 0.089 | 0.003 | 0.740 | 0.037 | See |
| 34 | KNeighborsClassifier | predict | 16.709 | 0.011 | 1000000 | 1000 | 2 | brute | 1 | 100 | 0.982 | 0.982 | 0.360 | 0.001 | 46.464 | 0.195 | See |
| 35 | KNeighborsClassifier | predict | 0.025 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 100 | 0.982 | 0.982 | 0.006 | 0.000 | 4.167 | 0.241 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 3.231 | 0.042 | 1000000 | 1000000 | 10 | kd_tree | -1 | 1 | 0.987 | 0.984 | 0.708 | 0.014 | 4.567 | 0.110 | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 0.459 | 0.004 | 1000000 | 1000 | 10 | kd_tree | -1 | 1 | 0.987 | 0.984 | 0.107 | 0.002 | 4.273 | 0.071 | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | kd_tree | -1 | 1 | 0.987 | 0.984 | 0.000 | 0.000 | 8.842 | 4.038 | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 3.212 | 0.039 | 1000000 | 1000000 | 10 | kd_tree | -1 | 5 | 0.987 | 0.984 | 0.740 | 0.021 | 4.340 | 0.133 | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 0.869 | 0.007 | 1000000 | 1000 | 10 | kd_tree | -1 | 5 | 0.987 | 0.984 | 0.195 | 0.003 | 4.464 | 0.076 | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | kd_tree | -1 | 5 | 0.987 | 0.984 | 0.000 | 0.000 | 6.031 | 2.212 | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 3.186 | 0.043 | 1000000 | 1000000 | 10 | kd_tree | -1 | 100 | 0.987 | 0.984 | 0.701 | 0.009 | 4.546 | 0.083 | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 2.750 | 0.018 | 1000000 | 1000 | 10 | kd_tree | -1 | 100 | 0.987 | 0.984 | 0.576 | 0.005 | 4.776 | 0.053 | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 0.004 | 0.000 | 1000000 | 1 | 10 | kd_tree | -1 | 100 | 0.987 | 0.984 | 0.001 | 0.000 | 2.956 | 0.778 | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 3.257 | 0.033 | 1000000 | 1000000 | 10 | kd_tree | 1 | 1 | 0.987 | 0.984 | 0.745 | 0.018 | 4.372 | 0.115 | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 0.743 | 0.003 | 1000000 | 1000 | 10 | kd_tree | 1 | 1 | 0.987 | 0.984 | 0.106 | 0.001 | 7.045 | 0.102 | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 1 | 0.987 | 0.984 | 0.000 | 0.000 | 2.604 | 1.222 | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 3.290 | 0.039 | 1000000 | 1000000 | 10 | kd_tree | 1 | 5 | 0.987 | 0.984 | 0.699 | 0.007 | 4.705 | 0.072 | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1.446 | 0.002 | 1000000 | 1000 | 10 | kd_tree | 1 | 5 | 0.987 | 0.984 | 0.186 | 0.002 | 7.783 | 0.088 | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 5 | 0.987 | 0.984 | 0.000 | 0.000 | 2.199 | 1.046 | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 3.258 | 0.031 | 1000000 | 1000000 | 10 | kd_tree | 1 | 100 | 0.987 | 0.984 | 0.737 | 0.021 | 4.417 | 0.131 | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 4.712 | 0.033 | 1000000 | 1000 | 10 | kd_tree | 1 | 100 | 0.987 | 0.984 | 0.578 | 0.014 | 8.155 | 0.210 | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 100 | 0.987 | 0.984 | 0.001 | 0.000 | 1.435 | 0.541 | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1.318 | 0.049 | 1000000 | 1000000 | 2 | kd_tree | -1 | 1 | 0.987 | 0.984 | 0.498 | 0.016 | 2.648 | 0.131 | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 0.027 | 0.002 | 1000000 | 1000 | 2 | kd_tree | -1 | 1 | 0.987 | 0.984 | 0.001 | 0.000 | 37.528 | 16.736 | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | kd_tree | -1 | 1 | 0.987 | 0.984 | 0.000 | 0.000 | 21.368 | 17.747 | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1.278 | 0.019 | 1000000 | 1000000 | 2 | kd_tree | -1 | 5 | 0.987 | 0.984 | 0.494 | 0.018 | 2.586 | 0.103 | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 0.026 | 0.000 | 1000000 | 1000 | 2 | kd_tree | -1 | 5 | 0.987 | 0.984 | 0.001 | 0.000 | 24.233 | 8.467 | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | kd_tree | -1 | 5 | 0.987 | 0.984 | 0.000 | 0.000 | 19.470 | 15.038 | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1.313 | 0.025 | 1000000 | 1000000 | 2 | kd_tree | -1 | 100 | 0.987 | 0.984 | 0.504 | 0.017 | 2.607 | 0.101 | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 0.048 | 0.002 | 1000000 | 1000 | 2 | kd_tree | -1 | 100 | 0.987 | 0.984 | 0.007 | 0.000 | 7.151 | 0.474 | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | kd_tree | -1 | 100 | 0.987 | 0.984 | 0.000 | 0.000 | 19.184 | 14.440 | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1.264 | 0.031 | 1000000 | 1000000 | 2 | kd_tree | 1 | 1 | 0.987 | 0.984 | 0.497 | 0.016 | 2.545 | 0.105 | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 0.023 | 0.000 | 1000000 | 1000 | 2 | kd_tree | 1 | 1 | 0.987 | 0.984 | 0.001 | 0.000 | 30.698 | 13.613 | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 1 | 0.987 | 0.984 | 0.000 | 0.000 | 5.669 | 4.398 | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1.280 | 0.021 | 1000000 | 1000000 | 2 | kd_tree | 1 | 5 | 0.987 | 0.984 | 0.495 | 0.018 | 2.588 | 0.105 | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 0.031 | 0.007 | 1000000 | 1000 | 2 | kd_tree | 1 | 5 | 0.987 | 0.984 | 0.001 | 0.000 | 27.904 | 10.966 | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 5 | 0.987 | 0.984 | 0.000 | 0.000 | 5.729 | 4.670 | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1.300 | 0.046 | 1000000 | 1000000 | 2 | kd_tree | 1 | 100 | 0.987 | 0.984 | 0.492 | 0.016 | 2.644 | 0.126 | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 0.054 | 0.001 | 1000000 | 1000 | 2 | kd_tree | 1 | 100 | 0.987 | 0.984 | 0.008 | 0.002 | 7.021 | 1.859 | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 100 | 0.987 | 0.984 | 0.000 | 0.000 | 5.568 | 4.629 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 0.564 | 0.007 | 1000000 | 1000000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.466 | 0.040 | 1.210 | 0.105 | See |
| 1 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 0.967 | 0.561 | See |
| 2 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 2.343 | 2.122 | See |
| 3 | KMeans_tall | fit | 0.491 | 0.003 | 1000000 | 1000000 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.418 | 0.025 | 1.176 | 0.071 | See |
| 4 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1000 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 1.948 | 1.407 | See |
| 5 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 2.405 | 2.001 | See |
| 6 | KMeans_tall | fit | 6.270 | 0.024 | 1000000 | 1000000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 2.901 | 0.010 | 2.161 | 0.011 | See |
| 7 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 2.056 | 1.451 | See |
| 8 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 1.447 | 1.102 | See |
| 9 | KMeans_tall | fit | 5.783 | 0.013 | 1000000 | 1000000 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 2.755 | 0.014 | 2.099 | 0.012 | See |
| 10 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 2.115 | 1.519 | See |
| 11 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.001 | 0.000 | 0.000 | 1.710 | 1.397 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 0.324 | 0.015 | 10000 | 10000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 21 | 0.001 | 30 | 0.005 | 0.101 | 0.002 | 3.208 | 0.158 | See |
| 1 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 21 | 0.001 | 30 | 0.005 | 0.001 | 0.000 | 1.020 | 0.234 | See |
| 2 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 21 | 0.001 | 30 | 0.005 | 0.000 | 0.000 | 1.675 | 1.255 | See |
| 3 | KMeans_short | fit | 0.126 | 0.001 | 10000 | 10000 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.001 | 30 | 0.005 | 0.046 | 0.001 | 2.709 | 0.048 | See |
| 4 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1000 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.001 | 30 | 0.005 | 0.001 | 0.000 | 1.066 | 0.257 | See |
| 5 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.001 | 30 | 0.005 | 0.000 | 0.000 | 1.971 | 1.571 | See |
| 6 | KMeans_short | fit | 0.795 | 0.028 | 10000 | 10000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 23 | 0.001 | 20 | 0.005 | 0.361 | 0.025 | 2.205 | 0.172 | See |
| 7 | KMeans_short | predict | 0.002 | 0.001 | 10000 | 1000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 23 | 0.001 | 20 | 0.005 | 0.001 | 0.000 | 1.943 | 1.124 | See |
| 8 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 23 | 0.001 | 20 | 0.005 | 0.000 | 0.000 | 1.906 | 1.047 | See |
| 9 | KMeans_short | fit | 0.266 | 0.051 | 10000 | 10000 | 100 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.001 | 20 | 0.005 | 0.173 | 0.024 | 1.541 | 0.363 | See |
| 10 | KMeans_short | predict | 0.004 | 0.002 | 10000 | 1000 | 100 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.001 | 20 | 0.005 | 0.001 | 0.000 | 3.423 | 1.377 | See |
| 11 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 100 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.001 | 20 | 0.005 | 0.000 | 0.000 | 1.906 | 1.168 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | n_iter | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 10.655 | 0.024 | 1000000 | 1000000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 0.27 | 10.635 | 0.015 | 1.002 | 0.003 | See |
| 1 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 0.27 | 0.000 | 0.000 | 0.765 | 0.371 | See |
| 2 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 0.27 | 0.000 | 0.000 | 0.353 | 0.346 | See |
| 3 | LogisticRegression | fit | 0.753 | 0.017 | 1000 | 1000 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [27] | 0.27 | 0.733 | 0.026 | 1.028 | 0.044 | See |
| 4 | LogisticRegression | predict | 0.002 | 0.000 | 1000 | 100 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [27] | 0.27 | 0.003 | 0.000 | 0.584 | 0.095 | See |
| 5 | LogisticRegression | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [27] | 0.27 | 0.001 | 0.000 | 0.123 | 0.090 | See |
Shared hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | n_iter | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1.726 | 0.056 | 100000 | 100000 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.910 | 0.011 | 1.897 | 0.066 | See |
| 1 | Ridge | predict | 0.001 | 0.000 | 100000 | 1000 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.002 | 0.001 | 0.521 | 0.357 | See |
| 2 | Ridge | predict | 0.000 | 0.000 | 100000 | 1 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.000 | 0.000 | 0.669 | 0.747 | See |
| 3 | Ridge | fit | 1.079 | 0.010 | 1000000 | 1000000 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.230 | 0.010 | 4.696 | 0.213 | See |
| 4 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.000 | 0.000 | 0.689 | 0.473 | See |
| 5 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.000 | 0.000 | 0.651 | 0.740 | See |
Shared hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | False |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
{
"system_info": {
"python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1",
"setuptools": "49.6.0.post20210108",
"sklearn": "0.24.2",
"numpy": "1.20.2",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": null,
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
"prefix": "libopenblas",
"user_api": "blas",
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"version": "0.3.12",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}